Project Summary The spleen plays a key role in the human immune system but also clears senescent red blood cells (RBC) from the circulation and those altered by acquired or inherited diseases. In patients with sickle cell disease (SCD), the spleen is one of the first targets of pathogenic processes and a potential protector against major complications. Under hypoxic conditions, mutated sickle hemoglobin (HbS) polymerizes to fibers which increase both the stiffness and adhesion of RBC. Splenic filtration of altered RBC prone to sickling (a process that cannot be directly observed in human subjects) contributes to anemia and likely triggers acute splenic sequestration crises (ASSC). On the other hand, it potentially prevents complications associated with intravascular sickling. Self- amplified blockade of vessels with sickled RBCs is indeed a hallmark of vaso-occlusive crises, acute chest syndrome, and acute hepatic crises, that severely impact the life quality and expectancy of patients with SCD. We propose to formulate and validate a new predictive modeling framework for how the spleen filters altered RBC in SCD by synergistically integrating in silico, in vitro, ex vivo and in vivo data using multifidelity-based neural networks (NN). This will deliver predictive models that can continuously learn when new data become available, a paradigm shift in biomedical modeling. We will develop multiscale/multifidelity computational models (and corresponding NN implementations) that link sub-cellular, cellular, and vessel level phenomena spanning across four orders of magnitude in spatio-temporal scales. This scale coupling will be accomplished using a molecular dynamics/dissipative particle dynamics (MD/DPD) framework. We will validate these predictive computational models by data from in vitro and ex vivo experiments, and RBC quantitative features collected in SCD patients. Specifically, we will use three new spleen-on-a-chip microfluidic devices with oxygen control and the unique human spleen perfusion setup of our foreign partner, with the following aims: Aim 1: Develop and validate a splenic inter-endothelial slit filtration model; Aim 2: Develop new models of RBC macrophage adhesion and of phagocytosis in the spleen; Aim 3: Perform Spleen-on-a-Chip experiments and validation; Aim 4: Validate the predictive framework based on RBC samples from patients. Realization of our four Specific Aims will significantly increase our understanding of the complex pathogenic and protective roles of the spleen in SCD. Feeding our new multifidelity neural networks with morphological and functional measures of RBC circulating in SCD patients will lead to models for residual spleen function in SCD, which should help predict the risk of acute splenic sequestration crises, and guide optimal timing for Stem Cell Transplantation or Gene Therapy. The new paradigm in using deep learning tools to integrate data from different sources will be applicable to modeling many other blood diseases.